Rolling bearings are one of the most prevalent components in rotating machines and reciprocating machines [1]}.
Vibration-based fault detection has become the preferred technique for bearing fault diagnosis [2]}, [3]}.
Bearing vibrations are usually modeled as cyclostationary signals (or group-sparse periodic) [4]}, [5]}, [6]}.
When a localized defect occurs on the bearing, repetitive transients will be generated due to the passing of rollers over the defect
[7]}, [8]}.
These transients have repetitive structure and are usually submerged in background noise.
Many denoising methods have been introduced to extract fault features for the purpose of detecting faults in machines,
such as wavelet transform [9]}, [10]},
singular value decomposition (SVD) [11]},
time-frequency analysis methods [12]}, [13]},
empirical mode decomposition (EMD) [14]},
methods for blind extraction of a cyclostationary signal and spectral kurtosis (SK) [4]}, [5]}, [17]}.
If compound faults exist, then the observed vibration signals are rather complex and it is difficult to identify each fault using traditional signal processing methods.
